






'''simple docstring'''
import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCamelCase__			=      16
UpperCamelCase__			=      32




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   return int(x / 2**20				)


class 							_UpperCAmelCase    :




    def __enter__(      self	:							List[Any]      ):






       '''simple docstring'''






       gc.collect()
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
       lowercase_						:     Dict							     =  torch.cuda.memory_allocated()
       return self





    def __exit__(      self	:							Optional[Any]	,  *a	:							Any      ):






       '''simple docstring'''






       gc.collect()
       torch.cuda.empty_cache()
       lowercase_						:     Optional[int]							     =  torch.cuda.memory_allocated()
       lowercase_						:     int							     =  torch.cuda.max_memory_allocated()
       lowercase_						:     int							     =  bamb(self.end - self.begin      )
       lowercase_						:     List[str]							     =  bamb(self.peak - self.begin      )
       # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase = 16    ,		_UpperCamelCase = "bert-base-cased"    ,		_UpperCamelCase = 320    ,		_UpperCamelCase = 160    ,		):


   """simple docstring"""




   lowercase_						:     List[str]							     =  AutoTokenizer.from_pretrained(_UpperCamelCase				)
   lowercase_						:     List[str]							     =  load_dataset(
       "glue"    ,		"mrpc"    ,		split={"train": F"""train[:{n_train}]""", "validation": F"""validation[:{n_val}]"""}				)

   def tokenize_function(_UpperCamelCase				):
      # max_length=None => use the model max length (it's actually the default)
      lowercase_						:     Optional[int]							     =  tokenizer(examples["sentence1"]    ,		examples["sentence2"]    ,		truncation=_UpperCamelCase    ,		max_length=_UpperCamelCase				)
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   lowercase_						:     Union[str, Any]							     =  datasets.map(
       _UpperCamelCase    ,		batched=_UpperCamelCase    ,		remove_columns=["idx", "sentence1", "sentence2"]    ,		load_from_cache_file=_UpperCamelCase				)

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   lowercase_						:     Optional[int]							     =  tokenized_datasets.rename_column("label"    ,		"labels"				)

   def collate_fn(_UpperCamelCase				):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(_UpperCamelCase    ,		padding="max_length"    ,		max_length=128    ,		return_tensors="pt"				)
      return tokenizer.pad(_UpperCamelCase    ,		padding="longest"    ,		return_tensors="pt"				)

   # Instantiate dataloaders.
   lowercase_						:     Optional[Any]							     =  DataLoader(
       tokenized_datasets["train"]    ,		shuffle=_UpperCamelCase    ,		collate_fn=_UpperCamelCase    ,		batch_size=_UpperCamelCase				)
   lowercase_						:     List[Any]							     =  DataLoader(
       tokenized_datasets["validation"]    ,		shuffle=_UpperCamelCase    ,		collate_fn=_UpperCamelCase    ,		batch_size=_UpperCamelCase				)

   return train_dataloader, eval_dataloader




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Dict							     =  Accelerator()

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowercase_						:     Optional[int]							     =  config['''lr''']
   lowercase_						:     List[Any]							     =  int(config["num_epochs"]				)
   lowercase_						:     Any							     =  int(config["seed"]				)
   lowercase_						:     List[str]							     =  int(config["batch_size"]				)
   lowercase_						:     Tuple							     =  args.model_name_or_path

   set_seed(_UpperCamelCase				)
   lowercase_						:     Dict							     =  get_dataloaders(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		args.n_train    ,		args.n_val				)

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowercase_						:     Optional[Any]							     =  AutoModelForSequenceClassification.from_pretrained(_UpperCamelCase    ,		return_dict=_UpperCamelCase				)

   # Instantiate optimizer
   lowercase_						:     str							     =  (
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   lowercase_						:     Optional[Any]							     =  optimizer_cls(params=model.parameters()    ,		lr=_UpperCamelCase				)

   if accelerator.state.deepspeed_plugin is not None:
      lowercase_						:     Tuple							     =  accelerator.state.deepspeed_plugin.deepspeed_config[
          '''gradient_accumulation_steps'''
      ]
   else:
      lowercase_						:     Tuple							     =  1
   lowercase_						:     List[Any]							     =  (len(_UpperCamelCase				) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      lowercase_						:     List[str]							     =  get_linear_schedule_with_warmup(
          optimizer=_UpperCamelCase    ,		num_warmup_steps=0    ,		num_training_steps=_UpperCamelCase    ,		)
   else:
      lowercase_						:     Optional[Any]							     =  DummyScheduler(_UpperCamelCase    ,		total_num_steps=_UpperCamelCase    ,		warmup_num_steps=0				)

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   lowercase_						:     Optional[int]							     =  accelerator.prepare(
       _UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)

   # We need to keep track of how many total steps we have iterated over
   lowercase_						:     Optional[Any]							     =  0
   # We also need to keep track of the stating epoch so files are named properly
   lowercase_						:     List[str]							     =  0

   # Now we train the model
   lowercase_						:     Optional[Any]							     =  {}
   for epoch in range(_UpperCamelCase    ,		_UpperCamelCase				):
      with TorchTracemalloc() as tracemalloc:
         model.train()
         for step, batch in enumerate(_UpperCamelCase				):
            lowercase_						:     List[Any]							     =  model(**_UpperCamelCase				)
            lowercase_						:     List[str]							     =  outputs.loss
            lowercase_						:     Union[str, Any]							     =  loss / gradient_accumulation_steps
            accelerator.backward(_UpperCamelCase				)
            if step % gradient_accumulation_steps == 0:
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()

            overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
      accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin				)				)				)
      accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used				)				)
      accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked				)				)
      accelerator.print(
          "Total Peak Memory consumed during the train (max): {}".format(
              tracemalloc.peaked + bamb(tracemalloc.begin				)				)				)
      lowercase_						:     List[str]							     =  tracemalloc.peaked + bamb(tracemalloc.begin				)
      if args.peak_memory_upper_bound is not None:
         assert (
             train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
         ), "Peak memory usage exceeded the upper bound"

   accelerator.wait_for_everyone()
   if accelerator.is_main_process:
      with open(os.path.join(args.output_dir    ,		"peak_memory_utilization.json"				)    ,		"w"				) as f:
         json.dump(_UpperCamelCase    ,		_UpperCamelCase				)




def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   lowercase_						:     Dict							     =  argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."				)
   parser.add_argument(
       "--model_name_or_path"    ,		type=_UpperCamelCase    ,		default="bert-base-cased"    ,		help="Path to pretrained model or model identifier from huggingface.co/models."    ,		required=_UpperCamelCase    ,		)
   parser.add_argument(
       "--output_dir"    ,		type=_UpperCamelCase    ,		default="."    ,		help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."    ,		)
   parser.add_argument(
       "--peak_memory_upper_bound"    ,		type=_UpperCamelCase    ,		default=_UpperCamelCase    ,		help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."    ,		)
   parser.add_argument(
       "--n_train"    ,		type=_UpperCamelCase    ,		default=320    ,		help="Number of training examples to use."    ,		)
   parser.add_argument(
       "--n_val"    ,		type=_UpperCamelCase    ,		default=160    ,		help="Number of validation examples to use."    ,		)
   parser.add_argument(
       "--num_epochs"    ,		type=_UpperCamelCase    ,		default=1    ,		help="Number of train epochs."    ,		)
   lowercase_						:     Union[str, Any]							     =  parser.parse_args()
   lowercase_						:     List[Any]							     =  {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
   training_function(_UpperCamelCase    ,		_UpperCamelCase				)


if __name__ == "__main__":
 main()












'''simple docstring'''





import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
						from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import DPTImageProcessor


class  snake_case__      :




					def __init__(  self  :		Union[str, Any]				, _A  :		Optional[Any]				, _A  :		Dict=2				, _A  :		Optional[Any]=32				, _A  :		List[str]=16				, _A  :		str=3				, _A  :		List[Any]=True				, _A  :		Optional[int]=True				, _A  :		Optional[int]=32				, _A  :		Optional[Any]=4				, _A  :		Optional[int]=[0, 1, 2, 3]				, _A  :		List[Any]=4				, _A  :		Optional[int]=37				, _A  :		Optional[Any]="gelu"				, _A  :		Optional[Any]=0.1				, _A  :		Union[str, Any]=0.1				, _A  :		Dict=0.02				, _A  :		Optional[Any]=3				, _A  :		Union[str, Any]=[1, 3_84, 24, 24]				, _A  :		int=True				, _A  :		int=None				, )    ->							Tuple:
								UpperCAmelCase_      :       Tuple          =       parent
								UpperCAmelCase_      :       List[str]          =       batch_size
								UpperCAmelCase_      :       Union[str, Any]          =       image_size
								UpperCAmelCase_      :       List[Any]          =       patch_size
								UpperCAmelCase_      :       Dict          =       num_channels
								UpperCAmelCase_      :       str          =       is_training
								UpperCAmelCase_      :       List[str]          =       use_labels
								UpperCAmelCase_      :       Optional[Any]          =       hidden_size
								UpperCAmelCase_      :       str          =       num_hidden_layers
								UpperCAmelCase_      :       int          =       backbone_out_indices
								UpperCAmelCase_      :       str          =       num_attention_heads
								UpperCAmelCase_      :       List[Any]          =       intermediate_size
								UpperCAmelCase_      :       Union[str, Any]          =       hidden_act
								UpperCAmelCase_      :       Dict          =       hidden_dropout_prob
								UpperCAmelCase_      :       Union[str, Any]          =       attention_probs_dropout_prob
								UpperCAmelCase_      :       List[str]          =       initializer_range
								UpperCAmelCase_      :       int          =       num_labels
								UpperCAmelCase_      :       Union[str, Any]          =       backbone_featmap_shape
								UpperCAmelCase_      :       List[str]          =       scope
								UpperCAmelCase_      :       List[str]          =       is_hybrid
								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
								UpperCAmelCase_      :       List[Any]          =       (image_size // patch_size) ** 2
								UpperCAmelCase_      :       List[str]          =       num_patches + 1




					def 		A      (  self  :		Dict      )    ->							Optional[int]:
								UpperCAmelCase_      :       Optional[int]          =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

								UpperCAmelCase_      :       List[str]          =       None
								if self.use_labels:
											UpperCAmelCase_      :       Optional[int]          =       ids_tensor([self.batch_size, self.image_size, self.image_size]				, self.num_labels      )

								UpperCAmelCase_      :       Dict          =       self.get_config()

								return config, pixel_values, labels




					def 		A      (  self  :		int      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Optional[Any]          =       {
								    '''global_padding''': '''same''',
								    '''layer_type''': '''bottleneck''',
								    '''depths''': [3, 4, 9],
								    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
								    '''embedding_dynamic_padding''': True,
								    '''hidden_sizes''': [96, 1_92, 3_84, 7_68],
								    '''num_groups''': 2,
								}

								return DPTConfig(
								    image_size=self.image_size				, patch_size=self.patch_size				, num_channels=self.num_channels				, hidden_size=self.hidden_size				, num_hidden_layers=self.num_hidden_layers				, backbone_out_indices=self.backbone_out_indices				, num_attention_heads=self.num_attention_heads				, intermediate_size=self.intermediate_size				, hidden_act=self.hidden_act				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, is_decoder=_A				, initializer_range=self.initializer_range				, is_hybrid=self.is_hybrid				, backbone_config=_A				, backbone_featmap_shape=self.backbone_featmap_shape				, )




					def 		A      (  self  :		Union[str, Any]				, _A  :		List[Any]				, _A  :		Optional[int]				, _A  :		Optional[int]      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Optional[int]          =       DPTModel(config=_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       List[str]          =       model(_A      )
								self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)      )




					def 		A      (  self  :		Tuple				, _A  :		str				, _A  :		Tuple				, _A  :		Union[str, Any]      )    ->							List[str]:
								UpperCAmelCase_      :       List[Any]          =       self.num_labels
								UpperCAmelCase_      :       Any          =       DPTForDepthEstimation(_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       int          =       model(_A      )
								self.parent.assertEqual(result.predicted_depth.shape				, (self.batch_size, self.image_size, self.image_size)      )




					def 		A      (  self  :		List[str]				, _A  :		str				, _A  :		Union[str, Any]				, _A  :		int      )    ->							Dict:
								UpperCAmelCase_      :       int          =       self.num_labels
								UpperCAmelCase_      :       Optional[Any]          =       DPTForSemanticSegmentation(_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       Tuple          =       model(_A				, labels=_A      )
								self.parent.assertEqual(
								    result.logits.shape				, (self.batch_size, self.num_labels, self.image_size, self.image_size)      )





					def 		A      (  self  :		Dict      )    ->							Any:
								UpperCAmelCase_      :       Optional[int]          =       self.prepare_config_and_inputs()
								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       config_and_inputs
								UpperCAmelCase_      :       Tuple          =       {'''pixel_values''': pixel_values}
								return config, inputs_dict






@require_torch
class  snake_case__      (					UpperCamelCase     ,				UpperCamelCase     ,				unittest.TestCase):
					a_        =					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
					a_        =					(
					    {
					        "depth-estimation": DPTForDepthEstimation,
					        "feature-extraction": DPTModel,
					        "image-segmentation": DPTForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					a_        =					False
					a_        =					False
					a_        =					False




					def 		A      (  self  :		Tuple      )    ->							Tuple:
								UpperCAmelCase_      :       Any          =       DPTModelTester(self      )
								UpperCAmelCase_      :       Dict          =       ConfigTester(self				, config_class=_A				, has_text_modality=_A				, hidden_size=37      )




					def 		A      (  self  :		Optional[int]      )    ->							int:
								self.config_tester.run_common_tests()




					@unittest.skip(reason='''DPT does not use inputs_embeds'''      )
					def 		A      (  self  :		Union[str, Any]      )    ->							str:
								pass




					def 		A      (  self  :		str      )    ->							Dict:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       Union[str, Any]          =       model_class(_A      )
											self.assertIsInstance(model.get_input_embeddings()				, (nn.Module)      )
											UpperCAmelCase_      :       Dict          =       model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_A				, nn.Linear      )      )




					def 		A      (  self  :		List[Any]      )    ->							Tuple:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       Dict          =       model_class(_A      )
											UpperCAmelCase_      :       Optional[Any]          =       inspect.signature(model.forward      )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											UpperCAmelCase_      :       int          =       [*signature.parameters.keys()]

											UpperCAmelCase_      :       Any          =       ['''pixel_values''']
											self.assertListEqual(arg_names[:1]				, _A      )




					def 		A      (  self  :		List[str]      )    ->							str:
								UpperCAmelCase_      :       List[Any]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_A      )




					def 		A      (  self  :		str      )    ->							List[str]:
								UpperCAmelCase_      :       List[Any]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*_A      )




					def 		A      (  self  :		str      )    ->							List[Any]:
								UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_A      )




					def 		A      (  self  :		Tuple      )    ->							str:
								for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
														continue

											UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       self.model_tester.prepare_config_and_inputs_for_common()
											UpperCAmelCase_      :       Union[str, Any]          =       True

											if model_class in get_values(_A      ):
														continue

											UpperCAmelCase_      :       Optional[int]          =       model_class(_A      )
											model.to(_A      )
											model.train()
											UpperCAmelCase_      :       Any          =       self._prepare_for_class(_A				, _A				, return_labels=_A      )
											UpperCAmelCase_      :       Tuple          =       model(**_A      ).loss
											loss.backward()




					def 		A      (  self  :		Union[str, Any]      )    ->							Tuple:
								for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
														continue

											UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       self.model_tester.prepare_config_and_inputs_for_common()
											UpperCAmelCase_      :       List[Any]          =       False
											UpperCAmelCase_      :       Optional[Any]          =       True

											if model_class in get_values(_A      ) or not model_class.supports_gradient_checkpointing:
														continue
											UpperCAmelCase_      :       int          =       model_class(_A      )
											model.to(_A      )
											model.gradient_checkpointing_enable()
											model.train()
											UpperCAmelCase_      :       int          =       self._prepare_for_class(_A				, _A				, return_labels=_A      )
											UpperCAmelCase_      :       List[str]          =       model(**_A      ).loss
											loss.backward()




					def 		A      (  self  :		Optional[Any]      )    ->							Any:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs_for_common()

								UpperCAmelCase_      :       List[str]          =       _config_zero_init(_A      )
								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       int          =       model_class(config=_A      )
											# Skip the check for the backbone
											UpperCAmelCase_      :       List[str]          =       []
											for name, module in model.named_modules():
														if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																	UpperCAmelCase_      :       Union[str, Any]          =       [F"{name}.{key}" for key in module.state_dict().keys()]
																	break

											for name, param in model.named_parameters():
														if param.requires_grad:
																	if name in backbone_params:
																				continue
																	self.assertIn(
																	    ((param.data.mean() * 1e9).round() / 1e9).item()				, [0.0, 1.0]				, msg=F"Parameter {name} of model {model_class} seems not properly initialized"				, )




					@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
					def 		A      (  self  :		Tuple      )    ->							Optional[Any]:
								pass




					@slow
					def 		A      (  self  :		Any      )    ->							int:
								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											UpperCAmelCase_      :       Any          =       DPTModel.from_pretrained(_A      )
											self.assertIsNotNone(_A      )





					def 		A      (  self  :		Union[str, Any]      )    ->							str:
								# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
								UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[Any]          =       self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_      :       Optional[int]          =       '''add'''
								with self.assertRaises(_A      ):
											UpperCAmelCase_      :       Optional[Any]          =       DPTForDepthEstimation(_A      )






def __UpperCAmelCase   (					)    ->   List[Any]:
			UpperCAmelCase_      :       List[Any]          =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
			return image







@require_torch
@require_vision
@slow
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Union[str, Any]      )    ->							List[Any]:
								UpperCAmelCase_      :       List[Any]          =       DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''      )
								UpperCAmelCase_      :       Any          =       DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''      ).to(_A      )

								UpperCAmelCase_      :       Tuple          =       prepare_img()
								UpperCAmelCase_      :       Union[str, Any]          =       image_processor(images=_A				, return_tensors='''pt'''      ).to(_A      )

								# forward pass
								with torch.no_grad():
											UpperCAmelCase_      :       str          =       model(**_A      )
											UpperCAmelCase_      :       List[Any]          =       outputs.predicted_depth

								# verify the predicted depth
								UpperCAmelCase_      :       str          =       torch.Size((1, 3_84, 3_84)      )
								self.assertEqual(predicted_depth.shape				, _A      )

								UpperCAmelCase_      :       Tuple          =       torch.tensor(
								    [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]      ).to(_A      )

								self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00				, _A				, atol=1e-4      )      )


